A century of statistical Ecology.

As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists.

Journals must expand access to peer review data.

Empirical studies on peer review bias are primarily conducted by people from privileged groups and with affiliations with the journals studied. Data access is one major barrier to conducting peer review research. Accordingly, we propose pathways to broaden access to peer review data to people from more diverse backgrounds.

Integrated community models: A framework combining multispecies data sources to estimate the status, trends and dynamics of biodiversity.

Data deficiencies among rare or cryptic species preclude assessment of community-level processes using many existing approaches, limiting our understanding of the trends and stressors for large numbers of species. Yet evaluating the dynamics of whole communities, not just common or charismatic species, is critical to understanding and the responses of biodiversity to ongoing environmental pressures. A recent surge in both public science and government-funded data collection efforts has led to a wealth of biodiversity data. However, these data collection programmes use a wide range of sampling protocols (from unstructured, opportunistic observations of wildlife to well-structured, design-based programmes) and record information at a variety of spatiotemporal scales. As a result, available biodiversity data vary substantially in quantity and information content, which must be carefully reconciled for meaningful ecological analysis. Hierarchical modelling, including single-species integrated models and hierarchical community models, has improved our ability to assess and predict biodiversity trends and processes. Here, we highlight the emerging 'integrated community modelling' framework that combines both data integration and community modelling to improve inferences on species- and community-level dynamics. We illustrate the framework with a series of worked examples. Our three case studies demonstrate how integrated community models can be used to extend the geographic scope when evaluating species distributions and community-level richness patterns; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species. We implemented these worked examples using multiple software methods through the R platform via packages with formula-based interfaces and through development of custom code in JAGS, NIMBLE and Stan. Integrated community models provide an exciting approach to model biological and observational processes for multiple species using multiple data types and sources simultaneously, thus accounting for uncertainty and sampling error within a unified framework. By leveraging the combined benefits of both data integration and community modelling, integrated community models can produce valuable information about both common and rare species as well as community-level dynamics, allowing for holistic evaluation of the effects of global change on biodiversity.

Peer review perpetuates barriers for historically excluded groups.

Peer review is central to the scientific process and scientists' career advancement, but bias at various stages of the review process disadvantages some authors. Here we use peer review data from 312,740 biological sciences manuscripts across 31 studies to (1) examine evidence for differential peer review outcomes based on author demographics, (2) evaluate the efficacy of solutions to reduce bias and (3) describe the current landscape of peer review policies for 541 ecology and evolution journals. We found notably worse review outcomes (for example, lower overall acceptance rates) for authors whose institutional affiliations were in Asia, for authors whose country's primary language is not English and in countries with relatively low Human Development Indices. We found few data evaluating efficacy of interventions outside of reducing gender bias through double-blind review or diversifying reviewer/editorial boards. Despite evidence for review outcome gaps based on author demographics, few journals currently implement policies intended to mitigate bias (for example, 15.9% of journals practised double-blind review and 2.03% had reviewer guidelines that mentioned social justice issues). The lack of demographic equity signals an urgent need to better understand and implement evidence-based bias mitigation strategies.

Errors in aerial survey count data: Identifying pitfalls and solutions.

Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double-observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.

Begging behavior as an honest signal of need and parent-offspring association during the postfledging dependency period.

Honest signaling mechanisms can function to appropriate care to hungry offspring and avoid misdirected care of unrelated offspring. Begging, the behavior by which offspring solicit food and parental care, may be an honest signaling mechanism for need, as well as association of parents and offspring. Roseate terns (Sterna dougallii) exhibit prolonged parental care during the postbreeding staging period, offering an ideal system in which to study begging as an honest signaling mechanism. We conducted focal sampling during two premigratory staging seasons (2014 and 2015) at Cape Cod National Seashore, Massachusetts, USA to determine whether postfledging tern begging behavior was an honest signal for need and parent-offspring association. Based on honest signaling theory, we expected begging behavior to be highest during times of high perceived need, and we expected to see a decrease in begging behavior as young terns became increasingly independent of the care-giving parent. Also, we predicted that young terns would be more likely to beg at parents than nonparents. We found that young roseate terns begged at their parents more often than nonparents; however, they did not always beg at parents. Model predictions of begging probability showed a linear relationship between begging and time of day and date of season, such that begging increased with time of day and decreased with date of season, respectively. Our results provide evidence for honest parent-offspring interactions and are inconsistent with parent-offspring conflict theory but suggest that begging may play a complex role in postfledging parent-offspring interactions. OPEN RESEARCH BADGES: This article has been awarded Open Data, Open materials Badges. All materials and data are publicly accessible via the Open Science Framework at https://doi.org/10.5281/zenodo.2656718.